Accurate and reliable detection of heat in dairy cows is essential for a controlled reproduction and therefore, for maintaining milk production. Classical approaches like visual identification are no longer viable on large dairy herds. Several automated techniques of detection have been proposed, but expected results are only achieved by expensive or invasive methods, because practical methods are not reliable. We present a method that aims to be both practical and accurate. It is based on simple attributes extracted from 3D acceleration data and well known classifiers: multilayer perceptrons, support vector machines and decision trees. Results show promising detection ratios, above 90% in several configurations of the detection system. Best results are achieved with multilayer perceptrons. This information could be readily incorporated to the automated system in a dairy farm and help to improve its efficiency.

Clustering is fundamental to understand the structure of data. In the past decade the cluster ensembleproblem has been introduced, which combines a set of partitions (an ensemble) of the data to obtain a singleconsensus solution that outperforms all the ensemble members. However, there is disagreement about which arethe best ensemble characteristics to obtain a good performance: some authors have suggested that highly diï¬€erentpartitions within the ensemble are beneï¬ cial for the ï¬ nal performance, whereas others have stated that mediumdiversity among them is better. While there are several measures to quantify the diversity, a better method toanalyze the best ensemble characteristics is necessary. This paper introduces a new ensemble generation strategyand a method to make slight changes in its structure. Experimental results on six datasets suggest that this isan important step towards a more systematic approach to analyze the impact of the ensemble characteristics onthe overall consensus performance.

The representation of sound signals at the cochlea and au- ditory cortical level has been studied as an alternative to classical anal- ysis methods. In this work, we put forward a recently proposed feature extraction method called approximate auditory cortical representation, based on an approximation to the statistics of discharge patterns at the primary auditory cortex. The approach here proposed estimates a non- negative sparse coding with a combined dictionary of atoms calculated from clean signal and noise. The denoising is carried out on noisy signals by the reconstruction of the signal discarding the atoms corresponding to the noise. Results on synthetic and real data show that the proposed method improves the quality of the signals, mainly under severe degra- dation. This communication corresponds to a journal paper published in 2015 in DSP (Elsevier).

A ffects carry important information in human communication and decision making, and their use in technology have grown in the past years. Particularly, emotions have a strong e ect on physiology, which can be assessed by biomedical signals. This signals have the advantage that can be recorded continuously, but also can become intrusive. The present work introduce an emotion recognition scheme based only in photoplethysmography, aimed to lower invasiveness. The feature extraction method was developed for a realistic real-time context. Furthermore, a feature normalization procedure was proposed to reduce the daily variability. For classi cation, two well-known models were compared. The proposed algorithms were tested on a public database, which consist of 8 emotions expressed continuously by a single subject along diff erent days. Recognition tasks were performed for several number of emotional categories and groupings. Preliminary results shows a promising performance with up to 3 emotion categories. Moreover, the recognition of arousal and emotional events was improved for larger emotion sets.

External validation indexes allow similarities between two clustering solutions to be quantified. With classical external indexes, it is possible to quantify how similar two disjoint clustering solutions are, where each object can only belong to a single cluster. However, in practical applications, it is common for an object to have more than one label, thereby belonging to overlapped clusters; for example, subjects that belong to multiple communities in social networks. In this study, we propose a new index based on an intuitive probabilistic approach that is applicable to overlapped clusters. Given that recently there has been a remarkable increase in the analysis of data with naturally overlapped clusters, this new index allows to comparing clustering algorithms correctly. After presenting the new index, experiments with artificial and real datasets are shown and analyzed. Results over a real social network are also presented and discussed. The results indicate that the new index can correctly measure the similarity between two partitions of the dataset when there are different levels of overlap in the analyzed clusters.

Assessment of both grazing behavior and herbage intake are two very difficult tasks that can be concurrently accomplished by means of accurate detection, classification and measurement of grazing events such as chews, bites and chew-bites. It is well known that acoustic monitoring is among the best methods to automatically quantify and classify ingestive and rumination events in grazing animals. However, most existing methods of signal analysis appear to be computationally complex and costly, and are therefore difficult to implement. In this work, we present and test a novel analysis system called Chew-Bite Real-Time Algorithm (CBRTA) that works fully automatically in real-time to detect and classify ingestive events of grazing cattle. The system employs a directional wide-frequency microphone facing inwards on the forehead of animals, and a coupled signal analysis and decision logic algorithm that measures shape, amplitude, duration and energy of sound signals to iteratively detect and classify ingestive events. Performance and validation of the CBRTA was determined using two databases of grazing signals. Signals were recorded on dairy cows offered either, natural pasture (N=25), or experimental micro-swards in indoor controlled environment (N=50). The CBRTA exhibited a simple linear complexity capable to execute 50 times faster than real-time and without undermining overall recognition rate and accuracy when signals were processed at 4 kHz sampling frequency and 8 bits quantization. Furthermore, CBRTA was capable to detect ingestive events with a 97.4% success rate, while achieving up to 84.0% success for their classification as exclusive chews, bites or composite chew-bites. The methodology proposed with CBRTA has promising application in embedded microcomputer systems that necessarily depend on fast real-time execution to minimize computational load, power source and storage memory. Such a system can readily facilitate the transmission of processed data through wireless network or the storage in an onboard device.

The reproducibility of research in bioinformatics refers to the notion that new methodologies/ algorithms and scientific claims have to be published together with their data and source code, in a way that other researchers may verify the findings to further build more knowledge upon them. The replication and corroboration of research results are key to the scientific process and many journals are discussing the matter nowadays, taking concrete steps in this direction. In this journal itself, a very recent opinion note has appeared highlighting the increasing importance of this topic in bioinformatics and computational biology, inviting the community to further discuss the matter. In agreement with that article, we would like to propose here another step into that direction with a tool that allows the automatic generation of a web interface, named web-demo, directly from source code in a very simple and straightforward way. We believe this contribution can help make research not only reproducible but also more easily accessible. A web-demo associated to a published paper can accelerate an algorithm validation with real data, wide-spreading its use with just a few clicks.

Acoustic monitoring of the ingestive behavior of grazing sheep was used to study the determinants of intake rate and to estimate dry matter intake (DMI) based on biting and chewing sounds. Each of three crossbred ewes (85±6.0kg body weight) were tested in 16 treatments resulting from the factorial combination of two forage species (orchardgrass and alfalfa), two levels of biomass depletion (tall=30±0.79cm and short=14±0.79cm) and four numbers of bites (20, 40, 60 and 80 bites). During each grazing session biting and chewing sounds were recorded with a wireless microphone placed on the ewe's forehead and connected to a digital video camera for synchronized audio and video recording of ingestive behavior. Dry matter (DM) intake rate was higher for alfalfa than orchardgrass (9.4±0.64 vs. 7.8±0.58g/min, P<0.05) because of lower fiber content (434±14 vs 558±6.6g/kg DM, P<0.01) and consequently shorter chewing time and fewer chews per unit DM (11±1.0 vs. 14±1.0 chews, P<0.05) in alfalfa than in orchardgrass. There were no differences in DMI rate between tall and short plants (8.7±0.67 vs. 8.5±0.68g/min, P>0.05), because sheep increased biting rate (from 17±1.6 to 28±1.6 bites/min, P<0.01) as bite mass declined from tall to short plants (from 0.54±0.02 to 0.31±0.01g DM, P<0.01). Sheep compensated for the reduction in bite mass by allocating fewer chews per bite (from 6.0±0.46 to 3.8±0.47, P<0.05) and increasing total jaw movement rate (from 95±6.3 to 122±6.3 movements/min, P<0.05). Compound jaw movements (chew-bites) were observed in every grazing session. The number of chew-bites was higher for tall than short plants (0.52±0.05 vs. 0.25±0.04 chew-bites/bite, P<0.05). The total amount of energy in chewing sound in a grazing session was linearly related to DMI (root mean square error=6.1g, coefficient of variation=27%); 79% of the total variation in total amount of energy in chewing sound was due to DMI. Dry matter intake was estimated accurately by acoustic analysis. The best model to predict DMI from acoustic analysis had a prediction error equal to 4.1g (coefficient of variation=18%, R2=0.92). Chewing energy per bite and total amount of energy in chewing sound were the most important predictors because they integrate information about eating time and intake rate of forages. The results demonstrate that ingestive sounds contain valuable information to remotely monitor feeding behavior and estimate dry matter intake in grazing ruminants.